Arboreal Identification Supported by Fuzzy Modeling for Trunk Texture Recognition

Authors

  • Adriano Bressane Universidade Estadual Paulista
  • Felipe Hashimoto Fengler
  • Sandra Regina Monteiro Masalskiene Roveda
  • José Arnaldo Frutuoso Roveda
  • Antonio Cesar Germano Martins

DOI:

https://doi.org/10.5540/tema.2018.019.01.111

Keywords:

soft computing, image processing, pattern matching, bioinformatics

Abstract

Due to the natural variability of the arboreal bark there are texture patterns in trunk images with values belonging to more than one species. Thus, the present study analyzed the usage of fuzzy modeling as an alternative to handle the uncertainty in the trunk texture recognition, in comparison with other machine learning algorithms. A total of 2160 samples, belonging to 20 tree species from the Brazilian native deciduous forest, were used in the experimental analyzes. After transforming the images from RGB to HSV, 70 texture patterns have been extracted based on first and second order statistics. Secondly, an exploratory factor analysis was performed for dealing with redundant information and optimizing the computational effort. Then, only the first dimensions with higher cumulative variability were selected as input variables in the predictive modeling. As a result, fuzzy modeling reached a generalization ability that outperformed algorithms widely used in classification tasks, besides of obtaining an almost perfect agreement with the classifier with the best accuracy in the validation tests. Therefore, the fuzzy modeling can be considered as a competitive approach, with reliable performance in arboreal trunk texture recognition.

Author Biography

Adriano Bressane, Universidade Estadual Paulista

PhD in Environmental sciences by São Paulo State University - UNESP (in progress), Environmental engineer with Masters in Urban engineering by Federal University of São Carlos - UFSCAR, professional experience as professor and consultant in environmental projects. As a researcher is dedicated to computational intelligence and mathematical modeling applied to decision-making processes, such as pattern recognition and biometric identification, impact assessment, management schemes of green spaces, land reclamation and landscape analysis, perception and participatory diagnosis, among others, area in which is also recognized for the contribution made to Elseviers journal, as Journal of Environmental Management and Ecological Indicators referee, even as editorial board member of the International Journal of Management and Fuzzy Systems and Journal of Information Technology and Telecommunications.

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Published

2018-05-05

How to Cite

Bressane, A., Fengler, F. H., Roveda, S. R. M. M., Roveda, J. A. F., & Martins, A. C. G. (2018). Arboreal Identification Supported by Fuzzy Modeling for Trunk Texture Recognition. Trends in Computational and Applied Mathematics, 19(1), 111. https://doi.org/10.5540/tema.2018.019.01.111

Issue

Vol. 19 No. 1 (2018)

Section

Original Article

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